CN109085547B - Denoising method and related device for surface penetrating radar echo signal - Google Patents
Denoising method and related device for surface penetrating radar echo signal Download PDFInfo
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- CN109085547B CN109085547B CN201811124687.4A CN201811124687A CN109085547B CN 109085547 B CN109085547 B CN 109085547B CN 201811124687 A CN201811124687 A CN 201811124687A CN 109085547 B CN109085547 B CN 109085547B
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The embodiment of the application discloses a denoising method and a relevant device for a surface penetrating radar echo signal, which comprise the following steps: carrying out dimensionality reduction processing on a plurality of original echo signals x (m, n) of a surface-penetrating radar by a principal component analysis method to obtain a plurality of echo signals y' (m, k), wherein m is the number of sampling points, n is the number of channels of the original echo signals, and k is the number of channels of the dimensionality reduction echo signals; acquiring a characteristic vector matrix corresponding to a matrix formed by multi-channel echo signals y' (m, k); denoising the multi-channel echo signal y' (m, k) to obtain a denoised signal z (m, k); and multiplying the denoised signal z (m, k) by the eigenvector matrix to obtain a reconstructed signal x' (m, n), and taking the reconstructed signal as a denoised signal of the multi-channel original echo signal x (m, n). The method is used for improving the denoising calculation efficiency of the surface layer penetrating radar echo signal.
Description
Technical Field
The application relates to the technical field of signal processing, in particular to a surface penetrating radar echo signal denoising method and a related device.
Background
Surface Penetrating Radar (SPR) as a relatively new nondestructive testing device, transmits high-frequency electromagnetic pulse waves to a hidden area through an antenna, the high-frequency electromagnetic pulse waves are transmitted in a medium of the hidden area, and when an abnormal target body with electrical property difference, such as a cavity and a boundary Surface, is encountered, the high-frequency electromagnetic pulse waves are reflected and return to a receiving antenna. On the basis of processing and analyzing the received radar wave signals, the spatial position, the structure, the electrical property and the geometric form of a target body in a shielding area can be deduced according to the waveform, the amplitude, the double-stroke travel time and other parameters of the signals, so that the aim of detecting a hidden target body is fulfilled. The SPR has the characteristics of simple operation, flexible working mode, high detection precision, high detection efficiency and the like. With the continuous improvement of human cognitive demands on objective world and the continuous development of radar technology, surface-penetrating radars are widely applied to many fields such as archaeology, mineral resource exploration, geological disaster exploration, geotechnical engineering investigation, engineering quality detection, military exploration, gesture recognition in shielded areas and the like.
In the surface penetration radar detection, in order to obtain more reflected wave characteristics, recording is generally performed using a wide frequency band, so that various effective waves are recorded, various interference noises are inevitably recorded, and the recorded radar echo profile cannot accurately and clearly reflect a target body. In addition, because the medium of the shielding area is equivalent to a complex filter, when the electromagnetic pulse wave reaches the receiving antenna, the amplitude is attenuated, and the waveform is different from the original transmitted waveform. The SPR echo signal is composed of components such as a direct wave, an interface reflected wave, a target scattered wave, and random noise, and overlaps with each other in a frequency domain and a time domain, and is difficult to distinguish. In general, radar echo profiles that have not been processed cannot be interpreted in any way.
Therefore, the received surface penetration radar echo signal must be denoised to improve the data quality and provide a clear radar echo profile for further comprehensive inference and interpretation. However, the existing denoising method has a large calculation amount, thereby causing the technical problem of low calculation efficiency.
Disclosure of Invention
The embodiment of the application provides a denoising method and a relevant device for a surface-penetrating radar echo signal, which are used for improving the denoising calculation efficiency of the surface-penetrating radar echo signal.
In view of the above, a first aspect of the present application provides a method for denoising a surface-penetrating radar echo signal, including:
carrying out dimensionality reduction processing on a plurality of original echo signals x (m, n) of a surface-penetrating radar by a principal component analysis method to obtain a plurality of echo signals y' (m, k), wherein m is the number of sampling points, n is the number of tracks of the original echo signals, and k is the number of tracks of the dimensionality reduction echo signals;
acquiring a characteristic vector matrix corresponding to a matrix formed by multi-channel echo signals y' (m, k);
denoising the multi-channel echo signal y' (m, k) to obtain a denoised signal z (m, k);
and multiplying the denoised signal z (m, k) by the eigenvector matrix to obtain a reconstructed signal x' (m, n), and taking the reconstructed signal as a denoised signal of the multi-channel original echo signal x (m, n).
Preferably, the first and second electrodes are formed of a metal,
performing dimensionality reduction processing on a plurality of original echo signals x (m, n) of a surface-penetrating radar by a principal component analysis method to obtain a plurality of echo signals y' (m, k) specifically comprises:
firstly, carrying out standardization processing on a plurality of original echo signals x (m, n) of a surface layer penetrating radar, wherein m is the number of sampling points, n is the number of channels of the original echo signals, and k is the number of channels of a dimensionality reduction echo signal;
then, a multi-channel echo signal y' (m, k) with a principal component cumulative contribution rate greater than a preset contribution rate is extracted.
Preferably, the first and second electrodes are formed of a metal,
after normalizing the multiple channels of raw echo signals x (m, n) of the surface-layer penetrating radar, before extracting multiple channels of echo signals y "(m, k) with principal component cumulative contribution rates larger than a preset contribution rate from the multiple channels of raw echo signals x (m, n) of the surface-layer penetrating radar after the normalization, the method further comprises:
and acquiring the variance of each original echo signal x (m, n) after the normalization processing, taking the variance of the original echo signal x (m, n) as a corresponding weight, and performing weighting processing on each original echo signal x (m, n) to obtain a weighted signal y ' (m, n), so that a plurality of echo signals y ' (m, k) with principal component cumulative contribution rates larger than a preset contribution rate are extracted from the plurality of weighted signals y ' (m, n).
Preferably, the first and second electrodes are formed of a metal,
denoising the multichannel echo signal y ″ (m, k), and obtaining a denoised signal z (m, k) specifically includes:
and performing wavelet denoising on the multi-channel echo signal y' (m, k) by adopting a continuous function of which the dependent variable infinitely approaches to the dependent variable along with the increase of the independent variable as a threshold function to obtain a denoised signal z (m, k).
Preferably, the first and second electrodes are formed of a metal,
the threshold function is
Wherein sgn () is a sign function, Wj,kIs a function of the wavelet decomposition coefficients,
and lambda is the threshold value of the wavelet coefficient after threshold value quantization.
Preferably, the first and second electrodes are formed of a metal,
before performing dimensionality reduction processing on a plurality of original echo signals x (m, n) of a surface-penetrating radar by a principal component analysis method to obtain a plurality of echo signals y' (m, k), the method further includes:
and performing direct wave removing processing on a plurality of original echo signals x (m, n) of the surface layer penetrating radar.
The second aspect of the present application provides a surface layer penetrates through denoising device of radar echo signal, including:
the main component analysis unit is used for carrying out dimensionality reduction processing on a plurality of original echo signals x (m, n) of the surface-penetrating radar through a principal component analysis method to obtain a plurality of echo signals y' (m, k), wherein m is the number of sampling points, n is the number of tracks of the original echo signals, and k is the number of tracks of the dimensionality reduction echo signals;
the characteristic vector matrix acquisition unit is used for acquiring a characteristic vector matrix corresponding to a matrix formed by the multi-channel echo signals y' (m, k);
the de-noising unit is used for de-noising the multi-channel echo signals y' (m, k) to obtain de-noised signals z (m, k);
and the reconstruction unit is used for multiplying the denoised signal z (m, k) with the eigenvector matrix to obtain a reconstructed signal x' (m, n), and taking the reconstructed signal as a denoised signal of the multi-channel original echo signal x (m, n).
Preferably, the denoising apparatus further includes:
and the weighting processing unit is used for acquiring the variance of each original echo signal x (m, n) after the normalization processing, taking the variance of the original echo signal x (m, n) as a corresponding weight value, and performing weighting processing on each original echo signal x (m, n) to obtain a weighted signal y ' (m, n), so that a multichannel echo signal y ' (m, k) with a principal component cumulative contribution rate greater than a preset contribution rate is extracted from the multichannel weighted signal y ' (m, n).
A third aspect of the present application provides a denoising apparatus for a surface-penetrating radar echo signal, the apparatus including a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the steps of the denoising method according to the first aspect according to instructions in the program code.
A fourth aspect of the present application provides a computer-readable storage medium for storing program code for executing the denoising method according to the first aspect.
According to the technical scheme, the embodiment of the application has the following advantages:
in an embodiment of the present application, a method for denoising a surface-penetrating radar echo signal is provided, including: carrying out dimensionality reduction processing on a plurality of original echo signals x (m, n) of a surface-penetrating radar by a principal component analysis method to obtain a plurality of echo signals y' (m, k), wherein m is the number of sampling points, n is the number of tracks of the original echo signals, and k is the number of tracks of the dimensionality reduction echo signals; acquiring a characteristic vector matrix corresponding to a matrix formed by multi-channel echo signals y' (m, k); denoising the multi-channel echo signal y' (m, k) to obtain a denoised signal z (m, k); multiplying the denoised signal z (m, k) with the eigenvector matrix to obtain a reconstructed signal x' (m, n), and taking the reconstructed signal as a denoised signal of a plurality of channels of original echo signals x (m, n); because the embodiment of the application adopts the principal component analysis method to perform the dimensionality reduction processing on the multi-channel original echo signals of the surface penetrating radar, the multi-channel echo signals obtained by dimensionality reduction are subjected to de-noising processing, and then the de-noised signals corresponding to the original echo signals are reconstructed, the number of echo signal channels needing to be subjected to de-noising processing is reduced on the premise of ensuring effective de-noising, so that the calculated amount is reduced, and the de-noising calculation efficiency of the surface penetrating radar echo signals is improved.
Drawings
Fig. 1 is a schematic flowchart of a first embodiment of a method for denoising a surface-layer-penetrating radar echo signal according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a second embodiment of a method for denoising a surface-layer-penetrating radar echo signal according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a denoising device for a surface-layer penetrating radar echo signal in an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a denoising device for a surface-layer penetrating radar echo signal in an embodiment of the present application;
FIG. 5 is a forward model of surface penetration radar;
FIG. 6 is a B-scan plot of the surface-penetrating radar noise-free from the forward simulation of FIG. 5;
FIG. 7 is a B-scan graph of the surface-layer penetrating radar noise obtained by adding two-dimensional white Gaussian noise to the whole of FIG. 6;
FIG. 8 is a principal component contribution ratio of each echo signal calculated from the two-dimensional signal in FIG. 7;
FIG. 9 is a principal component cumulative contribution rate of each echo signal calculated from the two-dimensional signal in FIG. 7;
FIG. 10 is a reconstructed signal corresponding to the two-dimensional signal of FIG. 7;
FIG. 11 is a B-scan diagram corresponding to a signal obtained by testing a preset test line through a surface-penetrating radar in an application example of the present application;
FIG. 12 is a B-scan diagram after denoising the signal in FIG. 11 by using the denoising method provided in the embodiment of the present application;
FIG. 13 is a graph comparing a denoising error curve using the denoising method provided by the embodiment of the present application with a denoising error curve using an existing denoising method;
FIG. 14 is a comparison graph of a running time curve of a denoising method provided by an embodiment of the present application and a prior denoising method.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a first embodiment of a method for denoising a surface-layer penetrating radar echo signal according to an embodiment of the present application, where as shown in fig. 1, the method for denoising a surface-layer penetrating radar echo signal includes:
101, performing dimensionality reduction processing on a plurality of original echo signals x (m, n) of a surface-penetrating radar by using a principal component analysis method to obtain a plurality of echo signals y' (m, k), wherein m is the number of sampling points, n is the number of tracks of the original echo signals, and k is the number of tracks of the dimensionality reduction echo signals.
The principal component analysis method is a conventional technique, and therefore, will not be described in detail here.
And 102, acquiring a characteristic vector matrix corresponding to a matrix formed by the multi-channel echo signals y' (m, k).
And 103, denoising the multi-channel echo signals y' (m, k) to obtain denoised signals z (m, k).
It is understood that there are many denoising methods, which are not limited herein.
And 104, multiplying the denoised signal z (m, k) by the eigenvector matrix to obtain a reconstructed signal x' (m, n), and taking the reconstructed signal as a denoised signal of the multi-channel original echo signal x (m, n).
The embodiment of the application adopts a principal component analysis method to perform dimension reduction processing on the multi-channel original echo signals of the surface penetrating radar, performs denoising processing on the multi-channel echo signals obtained by dimension reduction, and then reconstructs the denoised signals corresponding to the original echo signals, thereby reducing the number of echo signal channels needing denoising processing on the premise of ensuring effective denoising, reducing the calculated amount and improving the denoising calculation efficiency of the surface penetrating radar echo signals.
Referring to fig. 2, fig. 2 is a flowchart illustrating a second embodiment of a method for denoising a surface-layer-penetrating radar echo signal according to an embodiment of the present application, where as shown in fig. 2, the method for denoising a surface-layer-penetrating radar echo signal includes:
It can be understood that the original echo signal includes a direct wave, and the direct wave may interfere with the identification of the echo signal, so the embodiment of the present application removes the direct wave first.
The normalization process is the first step in the principal component analysis method, and generally includes processes such as normalization and averaging.
In the echo signal, the signal intensity of the target signal is high, and the variance of the corresponding echo signal is also high, so that the magnitude of the variance can represent the correlation with the target signal to some extent.
In step 204, a multi-channel echo signal y "(m, k) with a principal component cumulative contribution rate greater than a preset contribution rate is then extracted.
It should be noted that the predetermined contribution rate may be the rootThe adjustment is carried out according to the actual need, and the calculation formula of the contribution rate is as follows
The cumulative contribution rate is calculated by
Wherein k is 1,2jIs the characteristic value of the j-th channel signal.
And step 206, performing wavelet denoising on the multichannel echo signals y' (m, k) by using a continuous function of which the dependent variable approaches the dependent variable infinitely along with the increase of the independent variable as a threshold function to obtain denoised signals z (m, k).
It should be noted that, in the existing wavelet denoising, a soft threshold function and a hard threshold function are mostly adopted; when a soft threshold function is adopted, constant deviation can be generated between wavelet coefficient reconstruction and an actual wavelet coefficient; when a hard threshold function is adopted, the wavelet coefficient reconstruction is discontinuous, so that the embodiment of the application adopts a continuous function of which the dependent variable infinitely approaches to the dependent variable along with the increase of the independent variable as the threshold function.
Specifically, the threshold function adopted in the embodiment of the present application is
Wherein sgn () is a sign function, Wj,kIs a function of the wavelet decomposition coefficients,
is wavelet coefficient after threshold value quantization, wherein lambda is threshold value, and the expression of threshold value lambda is
In the formula, ln () is a logarithmic function, σ is variance estimation of each channel of signal, and N is the number of samples of each channel of original echo signal.
Further, in the embodiment of the present application, db6 can be used as the wavelet basis function, and the number of decomposition layers is 3.
And step 207, multiplying the denoised signal z (m, k) by the eigenvector matrix to obtain a reconstructed signal x' (m, n), and taking the reconstructed signal as a denoised signal of the multi-channel original echo signal x (m, n).
In the embodiment of the application, because the new threshold function is adopted, the wavelet coefficient reconstruction is infinitely close to the actual wavelet coefficient by the application of the new threshold function, so that the calculation precision can be improved, and the denoising effect is improved.
In order to illustrate that the denoising method for the surface layer penetrating radar echo signal in the embodiment of the application has the advantage of good denoising effect, the following tests are specially performed:
in the embodiment of the application, a surface-penetrating radar forward modeling diagram shown in fig. 5 is adopted, as shown in fig. 5, the media have three layers, the thickness of the first layer and the second layer is 15cm, the thickness of the third layer is 10cm, the relative dielectric constants of the first layer and the second layer are 20, 10 and 15 respectively, a metal pipe with the radius of 4cm is placed in the second layer, the center distance between the center of the metal pipe and the upper surface of the second layer is 6cm, the transmitting antenna and the receiving antenna are located right above the center of the metal pipe, the moving step of the transmitting antenna and the receiving antenna is 2cm, a transmitting signal is a Ricker wavelet with the center frequency of 900MHz, the time window is 40ns, and the grid size is 0.25cm x 0.25 cm.
And (3) obtaining a scattering echo of a target area received by the receiving antenna by adopting a time domain finite difference method, wherein the scattering echo is shown in fig. 6, fig. 6 is a surface layer penetrating radar noise-free B-scan graph obtained by forward simulation of fig. 5, the channel number is 100, the sampling point number is 6784, and the surface layer penetrating radar noise-free B-scan graph in fig. 6 is used as a surface layer penetrating radar original echo signal x (m, n).
Then, in order to generate a noise-containing original echo signal of the surface-layer penetrating radar in a simulated manner, two-dimensional random white gaussian noise with SNR of 17 is integrally added, and a noise-containing B-scan graph of the surface-layer penetrating radar after the noise is added is shown in fig. 7.
Then, 100 original echo signals x (m, n) of which the surface layer penetrates through the radar are normalized, a variance of each normalized original echo signal x (m, n) is obtained, the variance of the original echo signal x (m, n) is used as a corresponding weight, weighting processing is performed on each original echo signal x (m, n) to obtain a weighted signal y '(m, n), and then a contribution rate and an accumulated contribution rate of each weighted signal y' (m, n) are calculated, specifically referring to fig. 8 and 9, fig. 8 is a principal component contribution rate of each echo signal obtained by calculating the two-dimensional signal in fig. 7, and fig. 9 is a principal component accumulated contribution rate of each echo signal obtained by calculating the two-dimensional signal in fig. 7.
And extracting 64-channel echo signals y '(m, k) with principal component cumulative contribution rate larger than 90% of preset contribution rate, and acquiring a feature vector matrix corresponding to a matrix formed by the 64-channel echo signals y' (m, k).
Then, carrying out wavelet denoising on 64 echo signals y' (m, k) to obtain 64 surface layer penetrating radar echo signals z (m, k), specifically selecting db6 as a wavelet basis function, the number of decomposition layers is 3, and the corresponding threshold value of each echo signal is according to the wavelet basis function
Calculating, and multiplying the denoised signal z (m, k) by the eigenvector matrix to obtain 100 reconstructed signals x' (m, n), which can be specifically referred to fig. 10, where fig. 10 is a reconstructed signal corresponding to the two-dimensional signal in fig. 7.
Comparing the reconstructed signal with the echo signal of the noise-free surface layer penetration radar shown in FIG. 5, and applying a formula
(s (m) is an original noise-free signal, and z (m) is a signal subjected to noise addition and denoising) calculating an average mean square error value, and obtaining an average mean square error of 0.0196, so that the denoising method for the surface layer penetrating radar echo signal in the embodiment of the application has the advantages of small error, high precision and good denoising effect.
In order to illustrate the denoising effect of the denoising method for the surface-layer penetrating radar echo signal in the embodiment of the present application, the surface-layer penetrating radar echo data of the preset survey line is collected as shown in fig. 11, fig. 11 is a B-scan diagram corresponding to a signal obtained by testing the preset survey line through the surface-layer penetrating radar in the application example of the present application, then the echo data is denoised by the denoising method for the surface-layer penetrating radar echo signal in the embodiment of the present application, the echo data after denoising is shown in fig. 12, fig. 12 is a B-scan diagram obtained by denoising the signal in fig. 11 by using the denoising method provided in the embodiment of the present application, and it can be seen from fig. 11 and 12 that fig. 12 is easier to see the contour and form of the preset survey line than fig. 11, so the denoising method in the embodiment of the present application has an obvious denoising effect.
In order to demonstrate that the denoising method for the surface-layer penetrating radar echo signal in the embodiment of the application has the advantages of high calculation efficiency and good denoising effect compared with the existing denoising method, the following tests are carried out:
in this test, the denoising method of the surface-layer penetrating radar echo signal according to the embodiment of the present application and the existing denoising method are used to denoise the noise-free surface-layer penetrating radar echo signal shown in fig. 5, the echo signals processed by the two denoising methods are compared with the noise-free surface-layer penetrating radar echo signal shown in fig. 5, and a formula is applied
Calculating the average mean square error value, and comparing the performances of the two denoising methods, wherein the setting range of the channel number is [10,100 ]]The number of points is 10, and an average mean square error curve and calculation time of the two denoising methods under different channel numbers are finally obtained, as shown in fig. 13 and 14, fig. 13 is a graph comparing the denoising method provided by the embodiment of the application with the denoising error variation curve using the existing denoising method; FIG. 14 is a comparison graph of a running time curve of a denoising method provided by an embodiment of the present application and a prior denoising method.
As can be seen from fig. 13, under the condition that the denoising method of the embodiment of the present application and the existing denoising method are performed by the same number, the average mean square error of the denoising method of the embodiment of the present application is lower, and the denoising effect is better. As can be seen from fig. 14, under the condition that the denoising method of the embodiment of the present application and the existing denoising method are performed in the same number, the denoising method of the embodiment of the present application has significantly lower operation time and higher efficiency.
Referring to fig. 3, a schematic structural diagram of a denoising device for a surface-layer penetrating radar echo signal in an embodiment of the present application is shown.
The embodiment of the application provides a device of making an uproar falls in radar echo signal is penetrated on top layer, includes:
a principal component analysis unit 301, configured to perform dimensionality reduction processing on multiple original echo signals x (m, n) of a surface-penetrating radar by using a principal component analysis method to obtain multiple echo signals y ″ (m, k), where m is a number of sampling points, n is a number of channels of the original echo signals, and k is a number of channels of the dimensionality reduction echo signals;
an eigenvector matrix obtaining unit 302, configured to obtain an eigenvector matrix corresponding to a matrix formed by the multiple echo signals y ″ (m, k);
a denoising unit 303, configured to perform denoising processing on the multi-channel echo signal y ″ (m, k) to obtain a denoised signal z (m, k);
and a reconstructing unit 304, configured to multiply the denoised signal z (m, k) with the eigenvector matrix to obtain a reconstructed signal x' (m, n), and use the reconstructed signal as a denoised signal of the multiple channels of original echo signals x (m, n).
Further, the denoising apparatus may further include:
the weighting processing unit 305 is configured to obtain a variance of each normalized original echo signal x (m, n), and perform weighting processing on each original echo signal x (m, n) by using the variance of the original echo signal x (m, n) as a corresponding weight, so as to obtain a weighted signal y '(m, n), so that a multichannel echo signal y ″ (m, k) with a principal component cumulative contribution rate greater than a preset contribution rate is extracted from the multichannel weighted signal y' (m, n).
Please refer to fig. 4, a schematic structural diagram of a denoising apparatus for a surface-layer penetrating radar echo signal in the embodiment of the present application.
The embodiment of the application provides a surface layer penetrates through equipment of removing noise of radar echo signal, and equipment includes processor 401 and memory 402:
the memory 402 is used for storing the program codes and transmitting the program codes to the processor 401;
the processor 401 is configured to execute the steps of a method for denoising a surface-penetrating radar echo signal according to the foregoing embodiments according to instructions in the program code.
The present application further provides a computer-readable storage medium for storing a program code for implementing any one of the above-mentioned methods for denoising a surface-penetrating radar echo signal according to the embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (8)
1. A denoising method for a surface penetration radar echo signal is characterized by comprising the following steps:
carrying out dimensionality reduction processing on a plurality of original echo signals x (m, n) of a surface-penetrating radar by a principal component analysis method to obtain a plurality of echo signals y' (m, k), wherein m is the number of sampling points, n is the number of channels of the original echo signals, and k is the number of channels of the dimensionality reduction echo signals;
acquiring a characteristic vector matrix corresponding to a matrix formed by multi-channel echo signals y' (m, k);
denoising the multi-channel echo signal y' (m, k) to obtain a denoised signal z (m, k);
multiplying the denoised signal z (m, k) with the eigenvector matrix to obtain a reconstructed signal x' (m, n), and taking the reconstructed signal as a denoised signal of a plurality of channels of original echo signals x (m, n);
the denoising processing of the multi-channel echo signal y "(m, k) to obtain a denoised signal z (m, k) specifically includes:
using a continuous function of the dependent variable approaching the dependent variable infinitely with the increase of the independent variable as a threshold function
Wherein sgn () is a sign function, Wj,kIs a function of the wavelet decomposition coefficients,
and performing wavelet denoising on the multichannel echo signal y' (m, k) to obtain a denoised signal z (m, k) by taking the wavelet coefficient after threshold quantization and taking lambda as a threshold.
2. The denoising method of claim 1, wherein performing dimensionality reduction on the multi-channel raw echo signals x (m, n) of the surface-penetrating radar by a principal component analysis method to obtain multi-channel echo signals y "(m, k) specifically comprises:
firstly, carrying out standardization processing on a plurality of original echo signals x (m, n) of a surface layer penetrating radar, wherein m is the number of sampling points, n is the number of channels of the original echo signals, and k is the number of channels of a dimensionality reduction echo signal;
then, a multi-channel echo signal y "(m, k) having a principal component cumulative contribution rate greater than a preset contribution rate is extracted.
3. The denoising method according to claim 2, wherein after normalizing the plurality of raw echo signals x (m, n) of the surface-layer penetrating radar, before extracting a plurality of echo signals y "(m, k) having a principal component cumulative contribution rate greater than a predetermined contribution rate from the normalized plurality of raw echo signals x (m, n) of the surface-layer penetrating radar, the method further comprises:
and acquiring the variance of each original echo signal x (m, n) after the normalization processing, taking the variance of the original echo signal x (m, n) as a corresponding weight, and performing weighting processing on each original echo signal x (m, n) to obtain a weighted signal y ' (m, n), so that a plurality of echo signals y ' (m, k) with principal component cumulative contribution rates larger than a preset contribution rate are extracted from the plurality of weighted signals y ' (m, n).
4. The denoising method according to claim 1, wherein before performing dimensionality reduction processing on the multi-channel raw echo signals x (m, n) of the surface-penetrating radar by a principal component analysis method to obtain multi-channel echo signals y "(m, k), the method further comprises:
and performing direct wave removing processing on a plurality of original echo signals x (m, n) of the surface layer penetrating radar.
5. A noise removing device for a surface penetration radar echo signal is characterized by comprising:
the main component analysis unit is used for carrying out dimensionality reduction processing on a plurality of original echo signals x (m, n) of the surface-penetrating radar through a principal component analysis method to obtain a plurality of echo signals y' (m, k), wherein m is the number of sampling points, n is the number of channels of the original echo signals, and k is the number of channels of the dimensionality reduction echo signals;
the characteristic vector matrix acquisition unit is used for acquiring a characteristic vector matrix corresponding to a matrix formed by the multi-channel echo signals y' (m, k);
the de-noising unit is used for de-noising the multi-channel echo signals y' (m, k) to obtain de-noised signals z (m, k);
and the reconstruction unit is used for multiplying the denoised signal z (m, k) with the eigenvector matrix to obtain a reconstructed signal x' (m, n), and taking the reconstructed signal as a denoised signal of the multi-channel original echo signal x (m, n).
6. The denoising apparatus according to claim 5, further comprising:
and the weighting processing unit is used for acquiring the variance of each original echo signal x (m, n) after the normalization processing, taking the variance of the original echo signal x (m, n) as a corresponding weight value, and performing weighting processing on each original echo signal x (m, n) to obtain a weighted signal y ' (m, n), so that a multichannel echo signal y ' (m, k) with a principal component cumulative contribution rate larger than a preset contribution rate is extracted from the multichannel weighted signal y ' (m, n).
7. A de-noising apparatus for a surface penetrating radar echo signal, the apparatus comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the denoising method of any one of claims 1-4 according to instructions in the program code.
8. A computer-readable storage medium for storing a program code for executing the denoising method according to any one of claims 1 to 4.
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